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We present a new hybrid physics-based machine-learning approach to reservoir modeling.
Mechanism of fluid displacement in sands
S. E. Buckley and M. C. Leverett · 1942
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Mechanism of fluid displacement in sands
Se E Buckley, MCi Leverett, et al · 1942
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Analysis of Decline Curves, 1945
J.J. Arps · 1945
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A mathematical theory of communication
C. E. Shannon · 1948
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Modification of welge’s method of shock front location in the buckley-leverett problem for nonzero initial condition (includes associated papers 15193 and 15282 and 15797 and 15915 and 16458)
MFN Mohsen et al · 1985
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Hyperbolic systems of conservation laws and the mathematical theory of shock waves
Peter D. Lax · 1989
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Approximation capabilities of multilayer feedforward networks
Kurt Hornik · 1991
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Lecture notes on multiphase flow in porous media, January 2009
Hamdi Tchelepi · 2009
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SPE 143875 Modeling , History Matching , Forecasting and Analysis of Shale Reservoirs Performance Using Artificial Intelligence Top-Down , Intelligent Reservoir Modeling for Shale Formations
S.D. Mohaghegh, O. Grujic, S. Zargari, and M. Kalantari · 2011
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Auto-encoding variational bayes
Max Welling Diederik P. Kingma · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dandelion Mané, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viégas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2015
Cited alongside, same era.
Deep learning for physical processes: Incorporating prior scientific knowledge
Emmanuel de Bezenac, Arthur Pajot, and Patrick Gallinari · 2017
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Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2017
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Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2017
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
Nicholas Zabaras Xiaoqing Shi Shaoxing Mo, Yinhao Zhu and JichunWu · 2018
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Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
Nicholas Zabaras Yinhao Zhua · 2018
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Pde-net: Learning pdes from data
Xianzhong Ma Bin Dong Zichao Long, Yiping Lu · 2018
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Deep hidden physics models: Deep learning of nonlinear partial differential equations
Maziar Raissi · 2018
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Automatic differentiation in machine learning: a survey
Alexey Andreyevich Radul Jeffrey Mark Siskind Atilim Gunes Baydin, Barak A. Pearlmutter · 2018
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Regularisation of neural networks by enforcing lipschitz continuity
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The expressive power of neural networks: A view from the width
Zhou Lu, Hongming Pu, Feicheng Wang, Zhiqiang Hu, and Liwei Wang · 2017
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Opening the black box of deep neural networks via information
Ravid Shwartz-Ziv and Naftali Tishby · 2017
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Began: Boundary equilibrium generative adversarial networks
David Berthelot, Thomas Schumm, and Luke Metz · 2017
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Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
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SPE-190104-MS Comparison of Decline Curve Analysis DCA with Recursive Neural Networks RNN for Production Forecast of Multiple Wells
J Sun, X Ma, M Kazi, and C S E Icon · 2018
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Solving differential equations using neural networks
M. M. Chiaramonte and M. Kiener · 2018
Cited alongside, same era.
Data-driven identification of parametric partial differential equations
Steven L. Brunton Samuel Rudy, Alessandro Alla and J. Nathan Kutz · 2018
Cited alongside, same era.
Henry Gouk, Eibe Frank, Bernhard Pfahringer, and Michael Cree · 2018
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Physics-informed deep generative models
Paris Perdikaris Liu Yang · 2018
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Physics-informed generative adversarial networks for stochastic differential equations
George Em Karniadakis Liu Yang, Dongkun Zhang · 2018
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Adversarial uncertainty quantification in physics-informed neural networks
Paris Perdikaris Yibo Yang · 2018
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Deep learning and process understanding for data-driven earth system science
Bjorn Stevens Martin Jung Joachim Denzler Nuno Carvalhais Markus Reichstein, Gustau Camps-Valls and Prabhat · 2019
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Deep convolutional encoder-decoder networks for uncertainty quantification of dynamic multiphase flow in heterogeneousmedia
Phaedon-Stelios Koutsourelakisb Paris Perdikaris Yinhao Zhua, Nicholas Zabaras · 2019
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